One of the forefronts of computing technology is speech recognition, because people often find speech to be familiar and convenient way to communicate information. With computerized applications controlling many aspects of daily activities from word processing to controlling appliances, providing speech recognition based interfaces for such applications is a high priority of research and development for many companies. Web site operators and other content providers are deploying voice driven interfaces for allowing users to browse their content. One of the more visible implementations of speech recognition is Interactive Voice Response (IVR) systems, where caller can interact with a computer application through natural speech as opposed to pressing telephone keys or other mechanic methods.
In a traditional speech recognition system, the audio from a phone call may be recorded, transcribed, and then used to directly train a new speech recognition system as part of a feedback loop for a datacenter. System developers may also purchase sampled recordings from data consolidators in order to generate/enhance their training models. In a local application environment, where the speech recognition system is installed, operated, and maintained by a party (e.g. a user) independent from the system developer, there is little incentive and significant privacy concerns for the user to provide the exact audio of what they said to the system developer. This may disadvantage the system developer's efforts to enhance and update the speech recognition product with accurate data.